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Efficient Neural Models for Visual Attention

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Computer Vision and Graphics (ICCVG 2010)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6374))

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Abstract

Human vision rely on attention to select only a few regions to process and thus reduce the complexity and the processing time of visual task. Artificial vision systems can benefit from a bio-inspired attentional process relying on neural models. In such applications, what is the most efficient neural model: spiked-based or frequency-based? We propose an evaluation of both neural model, in term of complexity and quality of results (on artificial and natural images).

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Author information

Authors and Affiliations

  1. ETIS - Neurocybernetic team, ENSEA - University Cergy-Pontoise - CNRS UMR 8051, F-95000, Cergy, France

    Sylvain Chevallier, Nicolas Cuperlier & Philippe Gaussier

Authors
  1. Sylvain Chevallier
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  2. Nicolas Cuperlier
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  3. Philippe Gaussier
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Editor information

Editors and Affiliations

  1. Polish-Japanese Institute of Information Technology, 86 Koszykowa St, 02-008, Warsaw, Poland

    Leonard Bolc

  2. Institute of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Faculty of Applied Informatics and Mathematics, Department of Informatics, Warsaw University of Life Sciences, Nowoursynowska 159, 02-776, Warsaw, Poland

    Leszek J. Chmielewski

  4. Polish-Japanese Institute of Information Technology, 86 Koszykowa St, 02-008 Warsaw, konradw@pjwstk.edu.pl, The Silesian University of Technology, Institute of Computer Science, Akademicka 16, 44-100 Gliwice, Polan

    Konrad Wojciechowski

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Chevallier, S., Cuperlier, N., Gaussier, P. (2010). Efficient Neural Models for Visual Attention. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2010. Lecture Notes in Computer Science, vol 6374. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15910-7_29

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  • DOI: https://doi.org/10.1007/978-3-642-15910-7_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15909-1

  • Online ISBN: 978-3-642-15910-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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